Using a Complex Systems Approach to Understanding Population Mental Health.

使用复杂的系统方法来了解人群心理健康。

基本信息

批准号：
MR/N014588/1
负责人：
Daniel Lewis
金额：
$ 35.05万
依托单位：
London School of Hygiene & Tropical Medicine
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FN014588%2F1
关键词：
Using Complex Systems Approach Understanding

项目摘要

There is a growing consensus that it is not just individual characteristics and behaviours that influence health and wellbeing, but the types of neighbourhoods we live in, and the opportunities they afford us. This consensus is reflected by published evidence of inequalities in people's life chances between different places, the Marmot review "Fair Society Healthy Lives" demonstrates by association that neighbourhoods that are more deprived also tend to have populations with lower life expectancies.The overall evidence for neighbourhood effects is somewhat limited, only recently has research begun to demonstrate an unambiguous association between neighbourhood and health. Health outcomes and behaviours studied have particularly focused on diet and physical activity, which have been linked to a range of neighbourhood contextual factors including access to green space, places to buy fresh fruit and vegetables (or conversely fast-food), and the 'walkability' of the built environment. Mental health and wellbeing has received markedly less focus, although some high quality studies have looked at the relationship between neighbourhood green space and wellbeing. Emerging epidemiological evidence has benefitted from researchers working across disciplines, particularly with geographers, in order to produce robust study designs. Nonetheless, evidence of a causal link between our changing neighbourhoods, and our changing health is rather less forthcoming, owing to the complexity of the social-ecological processes hypothesised to be taking place.This research project aims to make a significant contribution to empirical evidence for neighbourhood effects by applying 'systems thinking.' This means treating neighbourhoods as complex, dynamic multi-factorial processes, where existing research treats them as simple, stationary snapshots with limited depth. Specific training in 'Urban Informatics' will greatly augment my existing expertise in Geographic Information Science and Social Epidemiology, and give me the tools required to operationalise dynamic, rather than static, representations of neighbourhoods. Capturing how neighbourhoods change over time alongside how individuals change over time is fundamental to unpicking causality.In particular, the research will focus on the effects on mental health and wellbeing of people moving, expecting to move, or preferring to move, at both the neighbourhood scale, and the individual scale. We know that neighbourhoods are not equal, and yet in terms of population movement - people moving into or out of a neighbourhood - we tend to treat them equally. Studies have suggested that failing to account for this dynamic neighbourhood process - the residential movement of people - can have implications on our expectations for people's health through what are termed 'selection effects'. These neighbourhood compositional factors will be studied alongside contextual aspects of neighbourhoods, notably neighbourhood crime and disorder and neighbourhood economic insecurity, in order to explain how mental health and wellbeing is affected by neighbourhood social and environmental changes.This type of research is only just becoming possible, due to a range of complementary factors. Firstly, world-class training at New York University's Centre for Urban Science and Progress (NYU CUSP). Secondly, the availability of the UK's rich longitudinal "Understanding Society" survey data. Thirdly, the proliferation of open, and free government administrative data, and finally, the growing support from research councils in promoting 'Big Data' and informatics approaches. This fellowship takes advantage of all of these factors, with the hope of producing world-leading research on neighbourhoods and health.

越来越多的共识是，不仅会影响健康和福祉的个人特征和行为，而且我们所居住的社区类型以及他们负担的机会。这一共识反映在人们在不同地方之间生活机会不平等的公开证据中反映出，Marmot评论“公平的社会健康生活”证明了社会表明，被剥夺的邻里居民也倾向于具有较低的预期人群的人口。邻里效应的总体证据是有限的，只有最近才能在社区和健康之间表现出毫无意义的研究。所研究的健康结果和行为特别关注饮食和体育锻炼，这些饮食和体育锻炼与一系列社区上下文因素有关，包括获得绿色空间，购买新鲜水果和蔬菜的地方（或相反的快餐店）以及建筑环境的“步行性”。尽管一些高质量的研究研究了邻里绿色空间与福祉之间的关系，但心理健康和福祉的关注点显着降低了。新兴的流行病学证据受益于跨学科（尤其是地理学家）的研究人员，以生产强大的研究设计。尽管如此，由于我们不断变化的社区之间因果关系的证据，而不断变化的健康状况却不那么面临，这是由于所假设正在发生的社会生态过程的复杂性。这项研究项目旨在通过运用“系统思维”来为社区效应做出重大贡献。这意味着将社区视为复杂，动态的多因素过程，现有研究将它们视为简单的固定快照，深度有限。在“城市信息学”中进行的具体培训将大大增强我在地理信息科学和社会流行病学方面的现有专业知识，并为我提供操作邻里的动态而不是静态表示所需的工具。捕捉社区如何随着时间的流逝而随着时间的流逝而变化，这是对取消因果关系的基础。尤其是，研究将重点关注对心理健康和福祉的影响，人们搬家，期望移动或更喜欢在社区规模和个人规模上移动。我们知道，在人口流动方面，社区不平等 - 人们搬入社区 - 我们倾向于平等对待它们。研究表明，未能解释这种动态的邻里过程 - 人们的住宅运动 - 可以通过所谓的“选择效应”对我们对人们健康的期望产生影响。这些社区组成因素将与社区的上下文方面进行研究，尤其是邻里犯罪和障碍以及邻里经济不安全感，以解释如何受到社区社会和环境变化的影响。由于一系列互补因素，这种类型的研究只是成为可能的。首先，在纽约大学城市科学与进步中心（NYU CUSP）的世界一流培训。其次，英国丰富的纵向“理解社会”调查数据的可用性。第三，公开和自由政府行政数据的扩散，最后，研究委员会在促进“大数据”和信息学方法方面的支持日益增长。该奖学金利用了所有这些因素，希望对社区和健康进行世界领先的研究。